Private Remote Phase Estimation over a Lossy Quantum Channel

• URL: http://arxiv.org/abs/2511.09123v1
• Date: Thu, 13 Nov 2025 01:33:56 GMT
• Title: Private Remote Phase Estimation over a Lossy Quantum Channel
• Authors: Farzad Kianvash, Marco Barbieri, Matteo Rosati,
• Abstract summary: Private remote quantum sensing (PRQS) aims at estimating a parameter at a distant location by transmitting quantum states on an insecure quantum channel.<n>Previous results highlighted that one can bound the estimation performance in terms of the observed noise.<n>We propose and analyse a PRQS using, for the first time to our knowledge, continuous-variable states in the single-user setting.
• Score: 0.6041648831662462
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Private remote quantum sensing (PRQS) aims at estimating a parameter at a distant location by transmitting quantum states on an insecure quantum channel, limiting information leakage and disruption of the estimation itself from an adversary. Previous results highlighted that one can bound the estimation performance in terms of the observed noise. However, if no assumptions are placed on the channel model, such bounds are very loose and severely limit the estimation. We propose and analyse a PRQS using, for the first time to our knowledge, continuous-variable states in the single-user setting. Assuming a typical class of lossy attacks and employing tools from quantum communication, we calculate the true estimation error and privacy of our protocol, both in the asymptotic limit of many channel uses and in the finite-size regime. Our results show that a realistic channel-model assumption, which can be validated with measurement data, allows for a much tighter quantification of the estimation error and privacy for all practical purposes.

Related papers

• Communicating Properties of Quantum States over Classical Noisy Channels [29.003268364998927]
  We introduce shadow tomography-based transmission with unequal error protection (STT-UEP)<n>It enables efficient transmission of properties of quantum states, allowing decoder-side estimation of arbitrary observables.<n>We provide theoretical guarantees on estimation accuracy as a function of the bit error probability of the classical channel.
  arXiv  Detail & Related papers  (2025-12-04T15:42:27Z)
• Faithful and secure distributed quantum sensing under general-coherent attacks [0.0]
  We introduce, theoretically analyze, and experimentally demonstrate single- and two-way protocols for distributed sensing.<n>We introduce a safety-threshold mechanism that allows the protocol to proceed in low-noise cases and quantifying the potential tampering with respect to the ideal estimation procedure.<n>We demonstrate our protocols in a photonic-based implementation, observing that the possibility of guaranteeing a safety threshold may come at a significant price in terms of the estimation bias.
  arXiv  Detail & Related papers  (2025-05-05T12:48:41Z)
• Criteria for unbiased estimation: applications to noise-agnostic sensing and learnability of quantum channel [1.7635061227370266]
  We first consider quantum state estimation, where multiple parameters are encoded in a quantum state.<n>We derive two equivalent necessary and sufficient conditions for an unbiased estimation.<n>We extend our analysis to quantum channel estimation, where the goal is to estimate parameters characterizing an unknown quantum channel.
  arXiv  Detail & Related papers  (2025-03-21T17:59:48Z)
• Real-time adaptation of quantum noise channel estimates [0.0]
  We propose a method to perform real-time adaptation of noise channel estimates during the execution of a quantum algorithmic circuit.<n>We demonstrate and evaluate the technique, which can be seen as a protocol for real-time calibration of high-level gate error information.
  arXiv  Detail & Related papers  (2025-01-30T19:00:01Z)
• Bayesian Quantum Amplitude Estimation [46.03321798937855]
  We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation.<n>In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically characterize it and self-adapt.<n>We propose a benchmark for amplitude estimation algorithms and use it to test BAE against other approaches.
  arXiv  Detail & Related papers  (2024-12-05T18:09:41Z)
• Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation [46.48569176651487]
  We prove a weaker convergence condition for bootstrapped value estimation in certain cases, even with off-policy data.<n>We extend our results to learning with truncated trajectories, showing that convergence is achievable for all tasks with minor modifications.
  arXiv  Detail & Related papers  (2024-05-31T17:36:16Z)
• Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification [59.81904428056924]
  We introduce SMASH: a Score MAtching estimator for learning markedPs with uncertainty quantification. Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of markedPs through score-matching. The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification.
  arXiv  Detail & Related papers  (2023-10-25T02:37:51Z)
• Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing Uncertainty [125.41260574344933]
  We consider an encoder-decoder based deep ensemble network for capturing both perception and predictive uncertainty simultaneously. Overall, deep ensembles provided more robust predictions and the consideration of upstream uncertainty further increased the estimation accuracy for the model.
  arXiv  Detail & Related papers  (2023-05-26T04:27:48Z)
• Robust and efficient verification of graph states in blind measurement-based quantum computation [52.70359447203418]
  Blind quantum computation (BQC) is a secure quantum computation method that protects the privacy of clients. It is crucial to verify whether the resource graph states are accurately prepared in the adversarial scenario. Here, we propose a robust and efficient protocol for verifying arbitrary graph states with any prime local dimension.
  arXiv  Detail & Related papers  (2023-05-18T06:24:45Z)
• Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis [42.275148861039895]
  Variational quantum algorithms (VQAs) offer the most promising path to obtaining quantum advantages via noisy processors. gate noise due to imperfections and decoherence affects the gradient estimates by introducing a bias. Quantum error mitigation (QEM) techniques can reduce the estimation bias without requiring any increase in the number of qubits. QEM can reduce the number of required iterations, but only as long as the quantum noise level is sufficiently small.
  arXiv  Detail & Related papers  (2022-09-23T10:48:04Z)
• CertainNet: Sampling-free Uncertainty Estimation for Object Detection [65.28989536741658]
  Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size.
  arXiv  Detail & Related papers  (2021-10-04T17:59:31Z)
• Fading channel estimation for free-space continuous-variable secure quantum communication [0.0]
  We show that uncertainty about whether the transmittance is fixed or not results in a lower key rate. If the total number of measurements is large, one can obtain using our method a key rate similar to the non-fluctuating channel even for highly fluctuating channels.
  arXiv  Detail & Related papers  (2020-11-09T12:40:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.